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Weighted local sharing and local clearing for multimodal optimisation

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Abstract

Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing with spatially structured populations and elitist replacement. In local sharing, the bias towards sharing and the influence of spatial structure is controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect large deme sizes must be used, but the opposite must be true if one wishes to maximise the influence of spatial population structure. This paper introduces two modifications to the local sharing method. The first alters local sharing so that parent selection and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect of fitness sharing is weighted according to the distance between individuals in the entire population structure. The second method replaces fitness sharing within demes with clearing to produce a method that we call local clearing. The proposed methods, as tested on several benchmark problems, demonstrate a level of efficiency that surpasses that of traditional fitness sharing and standard local sharing. Additionally, they offer a level of parameter robustness that surpasses other elitist niching methods, such as clearing. Through analysis of the local clearing method, we show that this parameter robustness is a result of the isolated nature of the demes in a spatially structured population being able to independently concentrate on subsets of the desired optima in a fitness landscape.

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Notes

  1. A von Neumann neighbourhood consists of the north, south, east and west neighbours of a given cell in a grid or torus.

  2. Other researchers have independently discovered the clearing method under the name of dynamic niche sharing (Miller and Shaw 1996) or species identification (Li et al. 2002) We will use the term clearing in this paper, as it is this name that appears most frequently in the literature.

References

  • Baker JE (1987) Reducing bias and inefficiency in the selection algorithm. In: Grefenstette JJ (ed) Genetic algorithms and their applications (ICGA’87). Lawrence Erlbaum Associates, Hillsdale, New Jersey, pp 14–21

  • Bird S, Li X (2006) Adaptively choosing niching parameters in a PSO. In: Keijzer M, Cattolico M, Arnold D, Babovic V, Blum C, Bosman P, Butz MV, Coello Coello C, Dasgupta D, Ficici SG, Foster J, Hernandez-Aguirre A, Hornby G, Lipson H, McMinn P, Moore J, Raidl G, Rothlauf F, Ryan C, Thierens D (eds) 2006 Genetic and evolutionary computation conference (GECCO’2006), vol 1. ACM Press, Seattle, Washington, USA, pp 3–10

  • Collins RJ, Jefferson DR (1991) Selection in massively parallel genetic algorithms. In: Belew RK, Booker LB (eds) Proceedings of the fourth international conference on genetic algorithms (ICGA’91). Morgan Kaufmann, San Francisco, pp 249–256

  • De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, MI. Dissertation Abstracts International 36(10), 5140B, University Microfilms Number 76-9381

  • Deb K, Goldberg DE (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of the third International conference on genetic algorithms. Morgan Kaufmann, San Mateo, CA, pp 42–50

  • Dick G (2008) Spatially-structured niching methods for evolutionary algorithms. PhD thesis, University of Otago

  • Dick G, Whigham PA (2006) Spatially-structured evolutionary algorithms and sharing: do they mix? In: Wang T-D, Li X, Chen S-H, Wang X, Abbass HA, Iba H, Chen G, Yao X (eds) 6th International conference on simulated evolution and learning, SEAL 2006. Lecture notes in computer science, vol 4247. Springer, Berlin, pp 457–464

  • Dick G, Whigham PA (2008) Spatially-structured sharing technique for multimodal problems. J Comput Sci Technol 23(1):64–76

    Article  Google Scholar 

  • Goldberg DE, Deb K, Horn J (1992) Massive multimodality, deception, and genetic algorithms. In: Männer R, Manderick B (eds) Parallel problem solving from nature, vol 2. Elsevier, Amsterdam, pp 37–46

  • Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multi-modal function optimisation. In: Proceedings of the 2nd international conference on genetic algorithms and their applications, pp 41–49

  • Horn J (1997) The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations. PhD thesis, University of Illinois at Urbana Champaign

  • Horn J, Goldberg DE (1995) Genetic algorithm difficulty and the modality of fitness landscapes. In: Whitley LD, Vose MD (eds) Foundations of genetic algorithms, vol 3. Morgan Kaufmann, San Francisco, pp 243–269

  • Ishibuchi H, Doi T, Nojima Y (2006) Effects of using two neighborhood structures in cellular genetic algorithms for function optimization. In: Runarsson TP, Beyer H-G, Burke EK, Guervos JJM, Whitley LD, Yao X (eds) Parallel problem solving from nature—PPSN IX, (9th PPSN’06). Lecture notes in computer science, vol 4193. Springer, Berlin, pp 949–958

  • Ishibuchi H, Tsukamoto N, Nojima Y (2008) Use of local ranking in cellular genetic algorithms with two neighbourhood structures. In: Li X, Kirley M, Zhang M, Green D, Ciesielski V, Abbass H, Michalewicz Z, Hendtlass T, Deb K, Tan KC, Branke J, Shi Y (eds) 7th International conference on simulated evolution and learning, SEAL 2008. Lecture notes in computer science, vol 5361. Springer, Berlin, pp 309–318

  • Kirley M, Stewart R (2007) An analysis of the effects of population structure on scalable multiobjective optimization problems. In: Thierens D (ed) Proceedings of the 2007 conference on genetic and evolutionary computation (GECCO 2007), vol 1. ACM Press, New York, pp 845–852

  • Li JP, Balazs ME, Parks GT, Clarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234

    Article  Google Scholar 

  • Mahfoud SW (1992) Crowding and preselection revisited. In: Männer R, Manderick B (eds) Parallel problem solving from nature, vol 2. Elsevier, Amsterdam, pp 27–36

  • Mahfoud SW (1995a) A comparison of parallel and sequential niching methods. In: Eshelman L (ed) Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 136–143

  • Mahfoud SW (1995b) Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA. IlliGAL Report 95001

  • Mahfoud SW (1995c) Population size and genetic drift in fitness sharing. In: Whitley LD, Vose MD (eds) Foundations of genetic algorithms, vol 3. Morgan Kaufmann, San Francisco, pp 185–224

  • Mayr E (1970) Populations, species and evolution; an abridgment of animal species and evolution. Harvard University Press, Cambridge

  • Miller BL, Shaw MJ (1996) Genetic algorithms with dynamic niche sharing for multimodal function optimization. In: International conference on evolutionary computation, pp 786–791

  • Mühlenbein H (1989) Parallel genetic algorithms, population genetics and combinatorial optimization. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms (ICGA’89). Morgan Kaufmann, San Francisco, pp 416–421

  • Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–458

    Article  Google Scholar 

  • Pétrowski A (1996) A clearing procedure as a niching method for genetic algorithms. In: Proceedings of the 1996 IEEE international conference on evolutionary computation, pp 798–803

  • Pétrowski A (1997) A new selection operator dedicated to speciation. In: Bäck T (ed) Proceedings of the seventh international conference on genetic algorithms (ICGA’97). Morgan Kaufmann, San Francisco, pp 144–151

  • Tomassini M (2005) Spatially structured evolutionary algorithms. Springer, Berlin

  • Watson J-P (1999) A performance assessment of modern niching methods for parameter optimization problems. In: Banzhaf W, Daida JM, Eiben AE, Garzon MH, Honavar V, Jakiela MJ, Smith RE (eds) Proceedings of the 1999 conference on genetic and evolutionary computation (GECCO 1999). Morgan Kaufmann, San Francisco, pp 702–709

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  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Grant Dick & Peter A. Whigham

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  2. Peter A. Whigham
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Correspondence to Grant Dick.

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Dick, G., Whigham, P.A. Weighted local sharing and local clearing for multimodal optimisation. Soft Comput 15, 1707–1721 (2011). https://doi.org/10.1007/s00500-010-0612-0

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